Heterogeneity-based models are prone to include outlier and controversial samples but performance-based approaches implicitly avoid such samples by estimating future errors. Representative sampling tends to query such instances that are representative of the overall input distribution, hence, avoid outliers and unrepresentative samples. These approaches weigh the dense input region to a higher degree while the querying process. Density-weighted techniques like information density are examples of representativeness sampling approaches that consider the representativeness of samples along with heterogeneity behavior, and are also known as hybrid models [1]}.
